{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
    "<dl class=\"dl-horizontal\">\n",
    "  <dt>Title</dt> <dd> Points Element</dd>\n",
    "  <dt>Dependencies</dt> <dd>Matplotlib</dd>\n",
    "  <dt>Backends</dt> <dd><a href='./Points.ipynb'>Matplotlib</a></dd> <dd><a href='../bokeh/Points.ipynb'>Bokeh</a></dd>\n",
    "</dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import holoviews as hv\n",
    "from holoviews import dim, opts\n",
    "\n",
    "hv.extension('matplotlib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``Points`` element visualizes as markers placed in a space of two independent variables, traditionally denoted ``x`` and ``y``. In HoloViews, the names ``'x'`` and ``'y'`` are used as the default key dimensions (``kdims``) of the element.  We can see this from the default axis labels when visualizing a simple ``Points`` element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12)\n",
    "coords = np.random.rand(50,2)\n",
    "points = hv.Points(coords)\n",
    "\n",
    "points.opts(color='k', marker='+', s=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here both the random ``x`` values and random ``y`` values are *both* considered to be the coordinates with no dependency between them (compare this to how [``Scatter``](./Scatter.ipynb) elements are defined). You can think of ``Points`` as simply marking positions in some two-dimensional space that can be sliced by specifying a 2D region-of-interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points + points[0.6:0.8,0.2:0.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the simplest ``Points`` element simply mark positions in a two-dimensional space without any associated value this doesn't mean value dimensions aren't supported. Here is an example with two additional quantities for each point, declared as the ``vdims``s ``z`` and ``size`` visualized as the color and size of the dots, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(10)\n",
    "data = np.random.rand(100,4)\n",
    "\n",
    "points = hv.Points(data, vdims=['z', 'size'])\n",
    "(points + points[0.3:0.7, 0.3:0.7].hist()).opts(\n",
    "    opts.Points(color='z', s=dim('size')*50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the right subplot, the ``hist`` method is used to show the distribution of samples along the first value dimension we added (*z*).\n",
    "\n",
    "\n",
    "The marker shape specified above can be any supported by [matplotlib](http://matplotlib.org/api/markers_api.html), e.g. ``s``, ``d``, or ``o``; the other options select the color and size of the marker.  For convenience with the [bokeh backend](../../../user_guide/Plotting_with_Bokeh.ipynb), the matplotlib marker options are supported using a compatibility function in HoloViews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Although the  ``Scatter`` element is superficially similar to the [``Points``](./Points.ipynb) element (they can generate plots that look identical), the two element types are semantically quite different. The fundamental difference is that [``Points``](./Points.ipynb) are used to visualize data where the *y* variable is *dependent*. This semantic difference also explains why the histogram generated by ``hist`` call above visualizes the distribution of a different dimension than it does for [``Scatter``](./Scatter.ipynb).\n",
    "\n",
    "This difference means that ``Points`` naturally combine elements that express independent variables in two-dimensional space, for instance [``Raster``](./Raster.ipynb) types such as [``Image``](./Image.ipynb). Similarly, ``Scatter`` expresses a dependent relationship in two-dimensions and combine naturally with ``Chart`` types such as [``Curve``](./Curve.ipynb).\n",
    "\n",
    "For full documentation and the available style and plot options, use ``hv.help(hv.Points).``"
   ]
  }
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